This workshop will explore well-motivated non-worst-case approaches to the analysis of algorithms and problems, as well as to the development of techniques that can take advantage of underlying structure in instances. It will bring together researchers from algorithms, learning theory, and AI, as well as application areas including SAT, formal verification, and sustainability. The themes of the workshop include: (i) learning commonalities of past instances to get an advantage in dealing with future instances; (ii) extracting features of different problem instances to learn which algorithmic approach to use; and (iii) finding natural assumptions, as in semi-random and smoothed-analysis models, which may allow for more efficient algorithms or tighter guarantees.